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  <div class="section" id="mindspore-nn-thor">
<h1>mindspore.nn.thor<a class="headerlink" href="#mindspore-nn-thor" title="Permalink to this headline">¶</a></h1>
<dl class="class">
<dt id="mindspore.nn.thor">
<em class="property">class </em><code class="sig-prename descclassname">mindspore.nn.</code><code class="sig-name descname">thor</code><span class="sig-paren">(</span><em class="sig-param">net</em>, <em class="sig-param">learning_rate</em>, <em class="sig-param">damping</em>, <em class="sig-param">momentum</em>, <em class="sig-param">weight_decay=0.0</em>, <em class="sig-param">loss_scale=1.0</em>, <em class="sig-param">batch_size=32</em>, <em class="sig-param">use_nesterov=False</em>, <em class="sig-param">decay_filter=&lt;function &lt;lambda&gt; at 0x0000029724CFA048&gt;</em>, <em class="sig-param">split_indices=None</em>, <em class="sig-param">enable_clip_grad=False</em>, <em class="sig-param">frequency=100</em><span class="sig-paren">)</span><a class="headerlink" href="#mindspore.nn.thor" title="Permalink to this definition">¶</a></dt>
<dd><p>通过二阶算法THOR更新参数。</p>
<p>基于跟踪的、硬件驱动层定向的自然梯度下降计算（THOR）算法论文地址为：</p>
<p><a class="reference external" href="https://www.aaai.org/AAAI21Papers/AAAI-6611.ChenM.pdf">THOR: Trace-based Hardware-driven layer-ORiented Natural Gradient Descent Computation</a></p>
<p>更新公式如下：</p>
<div class="math notranslate nohighlight">
\[\begin{split}\begin{array}{ll} \\
    A_i = a_i{a_i}^T \\
    G_i = D_{s_i}{ D_{s_i}}^T \\
    m_i = \beta * m_i + ({G_i^{(k)}}+\lambda I)^{-1}) g_i ({\overline A_{i-1}^{(k)}}+\lambda I)^{-1} \\
    w_i = w_i - \alpha * m_i \\
\end{array}\end{split}\]</div>
<p><span class="math notranslate nohighlight">\(D_{s_i}\)</span> 表示第i层输出的loss函数的导数。
<span class="math notranslate nohighlight">\(a_{i-1}\)</span> 表示第i层的输入，它是上一层的激活。
<span class="math notranslate nohighlight">\(\beta\)</span> 表示动量， <span class="math notranslate nohighlight">\(I\)</span> 代表单位矩阵。
<span class="math notranslate nohighlight">\(\overline A\)</span> 表示矩阵A的转置。
<span class="math notranslate nohighlight">\(\lambda\)</span> 表示’damping’， <span class="math notranslate nohighlight">\(g_i\)</span> 表示第i层的梯度。
<span class="math notranslate nohighlight">\(\otimes\)</span> 表示克罗内克尔积， <span class="math notranslate nohighlight">\(\alpha\)</span> 表示学习率。</p>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>在分离参数组时，如果权重衰减为正，则每个组的权重衰减将应用于参数。当不分离参数组时，如果 <cite>weight_decay</cite> 为正数，则API中的 <cite>weight_decay</cite> 将应用于名称中没有’beta’或 ‘gamma’的参数。</p>
<p>在分离参数组时，如果要集中梯度，请将grad_centralization设置为True，但梯度集中只能应用于卷积层的参数。
如果非卷积层的参数设置为True，则会报错。</p>
<p>为了提高参数组的性能，可以支持参数的自定义顺序。</p>
</div>
<p><strong>参数：</strong></p>
<ul class="simple">
<li><p><strong>net</strong> (Cell) - 训练网络。</p></li>
<li><p><strong>learning_rate</strong> (Tensor) - 学习率的值。</p></li>
<li><p><strong>damping</strong> (Tensor) - 阻尼值。</p></li>
<li><p><strong>momentum</strong> (float) - float类型的超参数，表示移动平均的动量。至少为0.0。</p></li>
<li><p><strong>weight_decay</strong> (int, float) - 权重衰减（L2 penalty）。必须等于或大于0.0。默认值：0.0。</p></li>
<li><p><strong>loss_scale</strong> (float) - loss缩放的值。必须大于0.0。一般情况下，使用默认值。默认值：1.0。</p></li>
<li><p><strong>batch_size</strong> (int) - batch的大小。默认值：32。</p></li>
<li><p><strong>use_nesterov</strong> (bool) - 启用Nesterov动量。默认值：False。</p></li>
<li><p><strong>decay_filter</strong> (function) - 用于确定权重衰减应用于哪些层的函数，只有在weight_decay&gt;0时才有效。默认值：lambda x: x.name not in []。</p></li>
<li><p><strong>split_indices</strong> (list) - 按A/G层（A/G含义见上述公式）索引设置allreduce融合策略。仅在分布式计算中有效。ResNet50作为一个样本，A/G的层数分别为54层，当split_indices设置为[26,53]时，表示A/G被分成两组allreduce，一组为0~26层，另一组是27~53层。默认值：None。</p></li>
<li><p><strong>enable_clip_grad</strong> (bool) - 是否剪切梯度。默认值：False。</p></li>
<li><p><strong>frequency</strong> (int) - A/G和$A^{-1}/G^{-1}$的更新间隔。当频率等于N（N大于1）时，A/G和$A^{-1}/G^{-1}$将每N步更新一次，和其他步骤将使用过时的A/G和$A^{-1}/G^{-1}$更新权重。默认值：100。</p></li>
</ul>
<p><strong>输入：</strong></p>
<ul class="simple">
<li><p><strong>gradients</strong> （tuple[Tensor]） - 训练参数的梯度，矩阵维度与训练参数相同。</p></li>
</ul>
<p><strong>输出：</strong></p>
<p>tuple[bool]，所有元素都为True。</p>
<p><strong>异常：</strong></p>
<ul class="simple">
<li><p><strong>TypeError</strong> - <cite>learning_rate</cite> 不是张量。</p></li>
<li><p><strong>TypeError</strong> - <cite>loss_scale</cite> 、 <cite>momentum</cite> 或 <cite>frequency</cite> 不是浮点数。</p></li>
<li><p><strong>TypeError</strong> - <cite>weight_decay</cite> 既不是浮点数也不是整数。</p></li>
<li><p><strong>TypeError</strong> - <cite>use_nesterov</cite> 不是布尔值。</p></li>
<li><p><strong>TypeError</strong> - <cite>frequency</cite> 不是整数。</p></li>
<li><p><strong>ValueError</strong> - <cite>loss_scale</cite> 小于或等于0。</p></li>
<li><p><strong>ValueError</strong> - <cite>weight_decay</cite> 或 <cite>momentum</cite> 小于0。</p></li>
<li><p><strong>ValueError</strong> - <cite>frequency</cite> 小于2。</p></li>
</ul>
<p><strong>支持平台：</strong></p>
<p><code class="docutils literal notranslate"><span class="pre">Ascend</span></code> <code class="docutils literal notranslate"><span class="pre">GPU</span></code></p>
<p><strong>样例：</strong></p>
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">mindspore.nn</span> <span class="kn">import</span> <span class="n">thor</span>
<span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">mindspore</span> <span class="kn">import</span> <span class="n">Model</span>
<span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">mindspore</span> <span class="kn">import</span> <span class="n">FixedLossScaleManager</span>
<span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">mindspore.train.callback</span> <span class="kn">import</span> <span class="n">LossMonitor</span>
<span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">mindspore.train.train_thor</span> <span class="kn">import</span> <span class="n">ConvertModelUtils</span>
<span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">mindspore</span> <span class="kn">import</span> <span class="n">nn</span>
<span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">mindspore</span> <span class="kn">import</span> <span class="n">Tensor</span>
<span class="go">&gt;&gt;&gt;</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">net</span> <span class="o">=</span> <span class="n">Net</span><span class="p">()</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">dataset</span> <span class="o">=</span> <span class="n">create_dataset</span><span class="p">()</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">temp</span> <span class="o">=</span> <span class="n">Tensor</span><span class="p">([</span><span class="mf">4e-4</span><span class="p">,</span> <span class="mf">1e-4</span><span class="p">,</span> <span class="mf">1e-5</span><span class="p">,</span> <span class="mf">1e-5</span><span class="p">],</span> <span class="n">mstype</span><span class="o">.</span><span class="n">float32</span><span class="p">)</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">optim</span> <span class="o">=</span> <span class="n">thor</span><span class="p">(</span><span class="n">net</span><span class="p">,</span> <span class="n">learning_rate</span><span class="o">=</span><span class="n">temp</span><span class="p">,</span> <span class="n">damping</span><span class="o">=</span><span class="n">temp</span><span class="p">,</span> <span class="n">momentum</span><span class="o">=</span><span class="mf">0.9</span><span class="p">,</span> <span class="n">loss_scale</span><span class="o">=</span><span class="mi">128</span><span class="p">,</span> <span class="n">frequency</span><span class="o">=</span><span class="mi">4</span><span class="p">)</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">loss</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">SoftmaxCrossEntropyWithLogits</span><span class="p">(</span><span class="n">sparse</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">reduction</span><span class="o">=</span><span class="s1">&#39;mean&#39;</span><span class="p">)</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">loss_scale</span> <span class="o">=</span> <span class="n">FixedLossScaleManager</span><span class="p">(</span><span class="mi">128</span><span class="p">,</span> <span class="n">drop_overflow_update</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">model</span> <span class="o">=</span> <span class="n">Model</span><span class="p">(</span><span class="n">net</span><span class="p">,</span> <span class="n">loss_fn</span><span class="o">=</span><span class="n">loss</span><span class="p">,</span> <span class="n">optimizer</span><span class="o">=</span><span class="n">optim</span><span class="p">,</span> <span class="n">loss_scale_manager</span><span class="o">=</span><span class="n">loss_scale</span><span class="p">,</span> <span class="n">metrics</span><span class="o">=</span><span class="p">{</span><span class="s1">&#39;acc&#39;</span><span class="p">},</span>
<span class="gp">... </span>              <span class="n">amp_level</span><span class="o">=</span><span class="s2">&quot;O2&quot;</span><span class="p">,</span> <span class="n">keep_batchnorm_fp32</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">model</span> <span class="o">=</span> <span class="n">ConvertModelUtils</span><span class="o">.</span><span class="n">convert_to_thor_model</span><span class="p">(</span><span class="n">model</span><span class="o">=</span><span class="n">model</span><span class="p">,</span> <span class="n">network</span><span class="o">=</span><span class="n">net</span><span class="p">,</span> <span class="n">loss_fn</span><span class="o">=</span><span class="n">loss</span><span class="p">,</span> <span class="n">optimizer</span><span class="o">=</span><span class="n">optim</span><span class="p">,</span>
<span class="gp">... </span>                                                <span class="n">loss_scale_manager</span><span class="o">=</span><span class="n">loss_scale</span><span class="p">,</span> <span class="n">metrics</span><span class="o">=</span><span class="p">{</span><span class="s1">&#39;acc&#39;</span><span class="p">},</span>
<span class="gp">... </span>                                                <span class="n">amp_level</span><span class="o">=</span><span class="s2">&quot;O2&quot;</span><span class="p">,</span> <span class="n">keep_batchnorm_fp32</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">loss_cb</span> <span class="o">=</span> <span class="n">LossMonitor</span><span class="p">()</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">model</span><span class="o">.</span><span class="n">train</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="n">dataset</span><span class="p">,</span> <span class="n">callbacks</span><span class="o">=</span><span class="n">loss_cb</span><span class="p">,</span> <span class="n">sink_size</span><span class="o">=</span><span class="mi">4</span><span class="p">,</span> <span class="n">dataset_sink_mode</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
</pre></div>
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